qte

Quantile Treatment Effects

Provides several methods for computing the Quantile Treatment
Effect (QTE) and Quantile Treatment Effect on the Treated (QTET). The main cases
covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as
randomly assigned after conditioning on some covariates (also called conditional
independence or selection on observables), (iii) Identification is based on a
Difference in Differences assumption (several varieties are available in the
package).

Brantly Callaway
2017-06-07
The R qte package implements many methods used, especially in economics, to estimate quantile treatment effects. These include the case where treatment is randomly assigned, under selection on observables, under a Difference in Differences Assumtpion.

The package is available on CRAN and can be loaded as follows

library(qte)

The following example shows how to use the ci.qte method in the qte package using data about an experimental job training program.

data(lalonde)

jt.cia <- ci.qte(re78 ~ treat,

xformla=~age + education + black + hispanic + married + nodegree,

data=lalonde.psid,

probs=seq(0.05,0.95,0.05), se=T)

summary(jt.cia)

More examples and details about other functions in the package can be found at the package's website